Machine Learning to Predict Mortality and Improve End-of-Life Outcomes among Minorities with Advanced Cancer

机器学习预测死亡率并改善少数晚期癌症患者的临终结局

基本信息

批准号：
10521902
负责人：
Cardinale B Smith
金额：
$ 30万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-20 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10521902
关键词：
Address Advanced Malignant Neoplasm Algorithms Attitude Belief Cancer Patient Caring Cessation of life Characteristics Clinical Decision Support Systems Data Development Discrimination Disease Documentation Educational workshop Electronic Health Record Emergency department visit Enrollment Ethnic Origin Feedback Focus Groups Goals Health Health system Healthcare Hospitalization Incidence Inferior Intervention Intervention Studies Interview Life Life Expectancy Link Machine Learning Malignant Neoplasms Minority Modality Modeling Multicenter Trials Nurses Oncologist Oncology Outcome Patient-Focused Outcomes Patients Perception Persons Physicians Predictive Value Procedures Process Prognosis Provider Race Randomized Controlled Trials Recommendation Reporting Research Resources Safety Social Workers Solid Solid Neoplasm Strategic Planning System Time Uncertainty base cancer care clinical decision-making clinical implementation data registry data tools design disparity reduction end of life experience health care service utilization high risk hospice environment improved improved outcome individual patient machine learning algorithm machine learning model machine learning prediction minority patient mortality mortality risk neoplasm registry predictive modeling predictive tools prognostic prognostic model prognostication racial bias standardized care tool

项目摘要

PROJECT SUMMARY/ABSTRACT Multiple studies show that minority patients with advanced cancer have inadequate discussions about treatment, prognosis and goals of care which contributes to higher utilization of health care among minorities at the end-of-life. A primary contributor to the low rates of prognosis and goals of care discussions relates to oncologists inability to accurately predict mortality. Clinical decision support systems (CDSS) are designed to directly aid clinical decision making by utilizing individual patient characteristics to generate patient-specific assessments. Limited studies indicate CDSS can reduce disparities in process of care and care standardization. However, existing tools do not identify patients at highest risk of mortality, have not been linked to patient outcomes and have not been routinely evaluated in minority patients. Machine learning (ML) predictive models allow more accurate prognoses by modeling patient and disease- specific interactions and has the potential to obviate the racial bias that exists in the use of oncologist- specific prognostication. ML models utilizing electronic health record (EHR) data can accurately predict short-term mortality among oncology patients. However, little evidence exists that these models assist with clinical decision making or improve outcomes for minority patients with cancer. We will address systemic race/ethnicity-related barriers that contribute to disparities in end-of-life outcomes among minority cancer patients by: 1) Developing and validating a predictive model to identify patients with advanced solid cancers at high risk of death within 90-days; 2) Creating a CDSS system intervention that incorporates mortality predictive tool data to prompt goals of care conversations for solid cancer patients at high risk of mortality within 90 days; and 3) Conducting a stepped-wedge cluster randomized controlled trial to evaluate whether implementing a clinical decision support system for patients with advanced solid cancer at risk of death within 90 days increases goals of care discussions and decreases utilization of aggressive care at the end-of-life among minorities versus non-minorities. The predictive model will be created from cancer registry data linked to the EHR. We will then create a CDSS by conducting focus groups among an interdisciplinary team of oncology clinicians (physicians, advance practice providers, nurses and social workers). Additionally, we will conduct co-design workshops with the oncology clinicians to inform the implementation of the CDSS. Next, we will conduct a stepped-wedge cluster randomized controlled trial to evaluate whether utilization of the CDSS increases goals of care discussions, and decreases healthcare utilization at the end-of-life among minority versus non-minority patients with advanced solid cancer at risk of death within 90 days. Finally, we will perform exit interviews to refine the intervention and study procedures. Findings will inform a larger multi-center trial aimed at implementation of the CDSS among those predicted to have high mortality to improve the end-of-life outcomes of minority patients with cancer.

项目摘要/摘要多项研究表明，晚期癌症患者的讨论不足治疗，预后和护理目标有助于更高利用医疗保健少数民族在生命末。导致预后率低和护理目标的主要贡献者讨论与肿瘤学家无法准确预测死亡率有关。临床决策支持系统（CDS）旨在通过利用个人患者特征来直接帮助临床决策产生特定于患者的评估。有限的研究表明，CDS可以减少在护理和护理标准化。但是，现有工具不能识别出死亡率最高的患者，尚未与患者结局联系在一起，也没有在少数族裔患者中进行常规评估。机器学习（ML）预测模型可以通过对患者和疾病进行建模，可以更准确特定的相互作用，并有可能消除使用肿瘤学家的种族偏见 - 特定的预后。使用电子健康记录（EHR）数据的ML模型可以准确预测肿瘤患者的短期死亡率。但是，几乎没有证据表明这些模型有助于癌症少数族裔患者的临床决策或改善结果。我们将解决系统性种族/民族相关的障碍，导致少数族裔癌症终结差异的差异患者作者：1）开发和验证预测模型以鉴定患有晚期固体癌症的患者在90天内死亡的高风险； 2）创建结合死亡率的CDSS系统干预预测性工具数据以促使对固体癌症患者的护理对话目标高死亡风险在90天内； 3）进行阶梯式北向群集随机对照试验进行评估是否针对患有晚期固体癌症患者的临床决策支持系统 90天内的死亡增加了护理讨论的目标，并降低了对积极护理的利用少数民族与非终点之间的寿命。预测模型将由癌症创建注册表数据链接到EHR。然后，我们将通过在肿瘤学临床医生的跨学科团队（医师，预先执业提供者，护士和社会工人）。此外，我们将与肿瘤学临床医生进行共同设计研讨会，以告知 CDS的实施。接下来，我们将进行阶梯式向外的群集随机对照试验评估CDS的利用是否会增加护理讨论的目标，并降低医疗保健少数族裔与非少数族患者的寿命末期利用在90天内死亡。最后，我们将进行退出访谈以完善干预和研究程序。调查结果将为旨在实施CDS的更大的多中心试验提供信息那些预计死亡率很高的人可以改善少数癌症患者的生命终结结果。